Abstract
It is crucial for software engineers to examine the impacts of their work, as the technologies they create influence both individual behavior and society. My capstone project, SousChef, is an application that teaches college students how to cook using a gamified approach to make the process more engaging and entertaining for users. My team chose this topic as it identified a core problem among many college students, lack of knowledge on how to make nutritious meals, and it gave us an opportunity to explore user design in web applications and how to increase user engagement in learning-based applications by using gamification. My STS Research Paper analyzes Generative AI and the effects it has on the career opportunities of artists. I undertook this research because the effects of Generative AI on artists act as a case study for how AI impacts the job market. My capstone project is relevant to the field of Computer Science, as it involves studying user design and how to engage users and promote learning using gamification in a digital platform. Similarly, Generative AI is a subfield of machine learning, examining how it functions and its effects are relevant, as such an analysis provides greater insight into how AI interacts with and affects society and job opportunities.
The main issue that SousChef addresses is teaching college students how to cook nutritious meals, while also aiming to keep users engaged. We implemented features inspired by other gamified learning apps like Duolingo, such as progress bars and confetti effects, to create a rewarding sense of progression. This model of showing progress, alongside featured lessons, gave a clear progression path for users to follow.
Our application, SousChef, successfully created a platform that used gamification elements to aid beginners in learning how to cook. When evaluating the application, users found the lesson structure clear to follow, with the gamification features providing satisfaction and encouraging users to continue using the platform. The specific features we added such as progress bars, and other visual effects were stated by users to be visually appealing and gave them an incentive to continue learning with different lessons. These results also showed that gamification provides a strong incentive that encourages users to continue learning.
As stated earlier, my STS Research Paper asks how Generative AI affects the career opportunities of artists. The significance of this research is that it provides insight into how AI can affect job opportunities for the fields it impacts, as Generative AI displacing artists provides an example for how it could impact other fields. The main methodology I used was using the analytical framework of Actor Network Theory alongside literature review to examine how different actors responded to the introduction of Generative AI, and how their actions interact with each other. I used this framework and conducted a literature review to gain evidence in three categories, legal analyses, technical analyses, and evidence of impacts on artists.
The literature review I conducted in my paper constitutes the main evidence I used to answer how Generative AI is affecting artists. The legal analyses displayed copyright issues that Generative AI potentially has regarding its use of other artists' art for training data without their permission. The technical analyses showed how the volume of training data and the art used is fundamental to the functionality of Generative AI. The evidence regarding the impacts on artists shows that Generative AI is a disruptive actor, displacing artists and removing work opportunities for artists. The results showed that under Actor Network Theory, Generative AI disrupted the previous network where companies would interact with artists, with generative AI companies filling the role that artists previously filled. The main conclusion reached in the paper is that Generative AI has a negative effect on artists, displacing them and reducing their opportunities for work.
